Atherosclerotic coronary disease is the most common cause of mortality in the United States. Mouse models of atherosclerosis have been very informative in elucidating the pathogenesis of atherosclerosis and in the identification of atherosclerosis modifying genes by testing candidate genes through their under or over expression. Additionally, unbiased but laborious genetic methods have been used to map the loci of mouse genes that alter atherosclerosis susceptibility;and, the identity of these genes are now being discerned. Atherosclerosis modifying genes discovered by the unbiased genetic method may illuminate pathways not previously known to be involved in atherogenesis. We have used a panel of apoE-deficient mice bred onto 6 inbred background strains along with a computational method called "in silico quantitative trait loci (QTL) mapping" to predict the genomic location of mouse atherosclerosis susceptibility genes. We detected 5 loci that stood out as candidates for further study, two of which were previously identified as significant atherosclerosis modifying loci using a more traditional QTL method that involves breeding a large cohort of F2 mice. The overall goal of the proposed work is identify the atherosclerosis susceptibility genes that underlie each of these and newly discovered QTLs, and assess whether common polymorphism in the human orthologs of these genes are associated with cardiovascular disease. We propose to confirm these in silico QTLs by breeding an F2 cohort between apoE-deficient mice on the atherosclerosis susceptible DBA/2 strain and the atherosclerosis resistant AKR strain. For each mouse, atherosclerosis lesion area will be quantitatively assessed and a genome scan will be preformed in order to perform a traditional QTL analysis to confirm the in silico QTLs and identify additional QTLs. To validate each QTL, lesions will be assessed in secondary congenic lines in which the QTL interval from one strain is bred onto the other strain. These secondary congenic lines will be bred further to generate recombinants in order to fine map the QTL interval to approximately 1 Mb. All of the genes in this interval will be identified via the mouse draft genome sequence. Multiple parallel approaches will be used to identify the responsible gene including expression micorarrays, identifying strain specific polymorphisms in the genes of each interval, and testing candidate genes by overexpression in transgenic mice. Once identified, these genes will be candidates for human genetic association studies to identify common variations associated with cardiovascular disease.